Growth of Medicare Advantage After Plan Payment Reductions

This cohort study examines the association between the Affordable Care Act’s Medicare Advantage payment reductions and Medicare Advantage enrollment growth.


Benchmark Calculations
Our calculation of ACA-associated benchmark changes reflected the timing and mechanism of the ACA reforms. Before the ACA, county benchmarks were set as the greater of either the previous year's benchmark multiplied by the growth in national overall Medicare spending or the projected per capita county FFS spending. The ACA reset county benchmarks at a percentage of the projected per capita county FFS spending that was used before the ACA. The percentage factors were determined by quartile of projected per capita county FFS spending and were set at 95%, 100%, 107.5% and 115%, with the lowest quartile spending counties received the largest percentage factors. To ease the financial transition for MA insurers, a transition period smoothed the payment changes over several years. Depending on the difference between the ACA and pre-ACA rates, each county was given a two, four, or six-year transition period where the effective benchmarks would be a blended mix of the pre-ACA and post-ACA benchmarks. Counties with larger differences were given longer transition periods. All counties transitioned fully to ACAset benchmarks by 2017.
For each county, we calculated a single quantity representing the magnitude of the ACAassociated benchmark cuts. We refer to this quantity as the ACA-induced benchmark cut. This quantity was calculated for each county as 2017 − 2017 2017 where 2017 is the 2017 benchmark calculated using the new ACA methods and 2017 is the counterfactual 2017 benchmark calculated using the pre-ACA methods. We did not simulate the counterfactual benchmark for each county because CMS includes this counterfactual benchmark value in its public data.

Sample Construction and Weighting for Secondary Payment Analysis
The sample construction necessarily differed between our primary enrollment analysis and our secondary payment analysis for two reasons. First, CMS Part C Plan Payment files are organized at the year-county-plan type level. Second, while enrollment and benchmark data are complete for counties with zero MA plan participation, payment data are not complete for such counties.
Thus, our sample included year-county-plan type combinations present in the 2008-2019 Part C Plan Payment files, excluding those with missing payment rates. Furthermore, because we used 2011 enrollment data to generate weights for county-plan type observations, we excluded counties with zero MA enrollment in 2011. This yielded 134,821 year-county-plan type observations, representing 35,763 county-years.
Because MA enrollment can vary widely across county and plan type, we weighted county-plan type observations by an imputed estimate of MA enrollment that was based on 2011 MA enrollment data. Briefly, we constructed weights based on the county-specific count of MA enrollees and the national proportions of MA enrollees in each plan type. For counties that have enrollment in every MA plan type at some point during the panel, the weight for each countyplan type was simply the product of the total county MA enrollment and plan type's proportion of national enrollment. If a plan type was not present (i.e. not offered or no enrollees) in a county's panel, the plan-specific enrollment proportions were normalized to sum to 100% across plan types offered in the county's panel.
To calculate national enrollment proportions for each plan-type present in our payment data, we used three data sources: the CMS Monthly Contract and Enrollment Summary Report, the CMS Monthly Enrollment by Contract and the CMS Special Needs Plan Comprehensive Report 1,2,3 . Plan type categories were not consistent between payment and enrollment sources (see crosswalk below). Thus, different data sources were used to calculate national enrollment counts for different plan types in the payment data. We used the CMS Monthly Contract and Enrollment Summary to determine the national enrollment in MSA, PFFS, and Regional PPO plans. We used the CMS Monthly Enrollment by Contract to determine the national enrollment across all HMO and local PPO plan types. And finally, we used the SNP Comprehensive Report to determine enrollment in the various HMO, local PPO, and Regional PPO SNP plans. The HMO and local PPO plan enrollment was calculated as the difference between enrollment in all HMO and local PPO plans and their respective SNP plans. The national enrollment counts for these 13 plan types was used to calculate national proportional ratios, which were then applied for each county, normalized based on which plan types were offered in the county's panel. PFFS --Regional PPO Regional PPO --Regional PPO Chronic or Disabling Conditions --Regional PPO Chronic or Disabling Conditions Regional PPO Dual-Eligible --Regional PPO Dual-Eligible

Statistical Analyses
All regressions were performed in Stata version 16.1. For our primary analysis, regression specifications were of the following form: • Difference-in-differences model: where i indexes the county, t indexes the year, is MA penetration is a vector of county fixed effects, is a vector of year fixed effects, is a binary indicator for whether a county experienced a benchmark cut in the 75 th percentile or above, is an indicator for the ACA benchmark phase-in period (2012-2016) and is an indicator for the post-phase in period (2017 or later), and is the error term. 2 is the coefficient of interest corresponding to our effect estimate. In sensitivity analyses, we replaced with alternate binary indicators for larger benchmark cuts, dichotomized at the median benchmark cut, or the 90 th percentile benchmark cut. We also employed the ACA-induced benchmark cut, a continuous measure of benchmark cut exposure, in place of the binary measure. The continuous measure ranged in value from 0 to 1, where 1 would represent a 100% cut. In another sensitivity analysis, we removed the 1 term and redefined period as 2012 onward. We used robust variance estimators to account for clustering within counties.
• Parallel trends testing and adjustment: where is the continuous year indexed to 2011, is the difference in linear time trend between treatment and comparison groups prior to the ACA. 4 The mean of during the ACA period corresponds to the difference-in-differences estimate after adjustment for linear, nonparallel time trends in the pre-ACA period.
For our secondary analysis, regressions were of a similar form, but with payment rates as the outcome and i indexing county-plan type.

eFigure. County MA Growth Versus Benchmark Cuts
Note: Each dot represents one US county. Growth in MA penetration is the difference in MA penetration rates between 2011 to 2019. Benchmark cut is the ACA-induced benchmark cut, defined in eMethods. The red line represents the line of best fit.